Establishing Cause and Effect - Scientific Causality
A positive correlation is a relationship between two variables in which both variables either increase Correlation is not and cannot be taken to imply causation. This animation explains the concept of correlation and causation. If you are unable to access the video a Transcript .doc 26kb) has been. This paper aims to further these understandings by explaining the statistical principles and techniques that underlie valid studies of causal relationships. .. effect on children's psychological development of prenatal exposure to barbiturates.
Correlation | Simply Psychology
Saul McLeodupdated Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: A positive correlation is a relationship between two variables in which both variables either increase or decease at the same time.
An example would be height and weight. Taller people tend to be heavier. A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other.
Establishing Cause and Effect
An example would be height above sea level and temperature. As you climb the mountain increase in height it gets colder decrease in temperature. A zero correlation exists when there is no relationship between two variables. For example their is no relationship between the amount of tea drunk and level of intelligence.
A correlation can be expressed visually.
- Correlation and Causation
- Association VS. Causal relationships
This is done by drawing a scattergram - that is one can plot the figures for one variable against the figures for the other on a graph. When you draw a scattergram it doesn't matter which variable goes on the x-axis and which goes on the y-axis. Remember, in correlations we are always dealing with paired scores, so the values of the 2 variables taken together will be used to make the diagram. Decide which variable goes on each axis and then simply put a cross at the point where the 2 values coincide.
Some uses of Correlations Prediction If there is a relationship between two variables, we can make predictions about one from another. Validity Concurrent validity correlation between a new measure and an established measure. Reliability Test-retest reliability are measures consistent. Inter-rater reliability are observers consistent. Theory verification Predictive validity. When reading and interpreting statistics, one must take great care to understand exactly what the data and its statistics are implying — and more importantly, what they are not implying.
Unfortunately, analysing statistics, probabilities and risks is not a skill set wired into our human intuitionand so is all too easy to be led astray.
Entire books have been written on the subtle ways in which statistics can be misinterpreted or used to mislead. To help keep your guard up, here are some common slippery statistical problems that you should be aware of: Consider a hypothetical study comparing the health of a group of office-workers with the health of a group of astronauts.
Correlation and Causation
If the study shows no significant difference between the two — no correlation between healthiness and working environment — are we to conclude that living and working in space carries no long-term health risks for astronauts? The groups are not on the same footing: We would therefore expect them to be significant healthier than office workers, on average, and should rightly be concerned if they were not.Correlation vs. Cause and Effect
This is also known as the Will Rogers effect, after the US comedian who reportedly quipped: When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states. If diagnostic methods improve, some very-slightly-unhealthy patients may be recategorised — leading to the health outcomes of both groups improving, regardless of how effective or not the treatment is.
Picking and choosing among the data can lead to the wrong conclusions.
The skeptics see period of cooling blue when the data really shows long-term warming green. This is bad statistical practice, but if done deliberately can be hard to spot without knowledge of the original, complete data set. Consider the above graph showing two interpretations of global warming data, for instance. Or fluoride — in small amounts it is one of the most effective preventative medicines in history, but the positive effect disappears entirely if one only ever considers toxic quantities of fluoride.
For similar reasons, it is important that the procedures for a given statistical experiment are fixed in place before the experiment begins and then remain unchanged until the experiment ends.
Consider a medical study examining how a particular disease, such as cancer or Multiple sclerosis, is geographically distributed.
If the disease strikes at random and the environment has no effect we would expect to see numerous clusters of patients as a matter of course.
If patients are spread out perfectly evenly, the distribution would be most un-random indeed!